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#!/usr/bin/python
# Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from operator_wrapping import Operator
from tensor_printer import TensorPrinter
from option_printer import OptionPrinter
from perf_predictor import PerfPredictor
def GetStrTensorIndex(tensors):
return_string = "["
for idx in range(len(tensors)):
if idx != 0:
return_string += ", "
return_string += str(tensors[idx].tensor_idx)
return_string += "]"
return return_string
class OperatorPrinter(object):
def __init__(self, verbose, operator):
self.verbose = verbose
self.operator = operator
def PrintInfo(self, perf_predictor=None):
if (self.verbose < 1):
return
op_str = "Operator {0}: {1}".format(self.operator.operator_idx,
self.operator.opcode_str)
if self.verbose == 2:
# total instruction num
instrs = "{:,}".format(self.operator.operation.TotalInstrNum()
) if self.operator.operation.can_compute else "???"
# total operation cycles
cycles = "{:,}".format(
(perf_predictor.PredictCycles(self.operator.operation))
) if self.operator.operation.can_compute and perf_predictor != None else "???"
op_str = op_str + "(instrs: {0}, cycls: {1})".format(instrs, cycles)
print(op_str)
print("\tFused Activation: " + self.operator.fused_activation)
self.PrintTensors()
def PrintTensors(self):
print("\tInput Tensors" + GetStrTensorIndex(self.operator.inputs))
for tensor in self.operator.inputs:
TensorPrinter(self.verbose, tensor).PrintInfo("\t\t")
print("\tOutput Tensors" + GetStrTensorIndex(self.operator.outputs))
for tensor in self.operator.outputs:
TensorPrinter(self.verbose, tensor).PrintInfo("\t\t")
# operator option
# Some operations does not have option. In such case no option is printed
OptionPrinter(self.verbose, self.operator.opcode_str,
self.operator.options).PrintInfo("\t")
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